Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/29883
Title: | An elastic-net penalized expectile regression with applications |
Authors: | Xu, QF Ding, XH Jiang, C Yu, K Shi, L |
Keywords: | expectile regression;elastic-net;SNCD;variable selection;high-dimensional data |
Issue Date: | 30-Jun-2020 |
Publisher: | Taylor & Francis |
Citation: | Xu, Q.F. et al. (2021) 'An elastic-net penalized expectile regression with applications', Journal of Applied Statistics, 48 (12), pp. 2205 - 2230. doi: 10.1080/02664763.2020.1787355. |
Abstract: | To perform variable selection in expectile regression, we introduce the elastic-net penalty into expectile regression and propose an elastic-net penalized expectile regression (ER-EN) model. We then adopt the semismooth Newton coordinate descent (SNCD) algorithm to solve the proposed ER-EN model in high-dimensional settings. The advantages of ER-EN model are illustrated via extensive Monte Carlo simulations. The numerical results show that the ER-EN model outperforms the elastic-net penalized least squares regression (LSR-EN), the elastic-net penalized Huber regression (HR-EN), the elastic-net penalized quantile regression (QR-EN) and conventional expectile regression (ER) in terms of variable selection and predictive ability, especially for asymmetric distributions. We also apply the ER-EN model to two real-world applications: relative location of CT slices on the axial axis and metabolism of tacrolimus (Tac) drug. Empirical results also demonstrate the superiority of the ER-EN model. |
Description: | Classification codes:: 62J05 The published version is freely available to all online at: https://www.tandfonline.com/doi/abs/10.1080/02664763.2020.1787355 . |
URI: | https://bura.brunel.ac.uk/handle/2438/29883 |
DOI: | https://doi.org/10.1080/02664763.2020.1787355 |
ISSN: | 0266-4763 |
Other Identifiers: | ORCiD: C.X. Jiang https://orcid.org/0000-0002-6900-8049 ORCiD: Keming Yu https://orcid.org/0000-0001-6341-8402 |
Appears in Collections: | Dept of Mathematics Research Papers |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
FullText.pdf | Copyright © 2020 Taylor & Francis. This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Applied Statistics on 30 Jun 2020, available at: https://www.tandfonline.com/10.1080/02664763.2020.1787355 (see: https://authorservices.taylorandfrancis.com/research-impact/sharing-versions-of-journal-articles/). | 931.76 kB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License